Images (i.e., single photographs or frames/pictures in video sequences) captured by imaging sensors in digital cameras often contain large amounts of random noise, especially in low-light conditions, that degrades image quality. For example, the noise may adversely affect the quality of the output of image processing tasks performed in the Bayer domain, such as color correction and demosaicing. In another example, as imaging sensor technology improves, the resolution of the captured images increases. When the resolution increases, the pixel size becomes smaller and the spatial frequency of the noise in the captured images becomes lower. Low frequency spatial noise may severely affect image quality, especially in the chroma channel.
Typically, one or more image noise filters are applied to the captured images at various points (e.g., in the Bayer domain and/or the color space domain (e.g., YCbCr or YUV)) during the processing of the images to reduce the noise and improve visual quality. For example, a spatial filter may be applied in the color space domain to reduce low-frequency noise. Typically, a spatial filter with large spatial support is needed to remove low-frequency noise. However, this type of filter is expensive to implement. One known approach is to downsample the image to reduce the resolution and then apply a spatial filter with small support to the downsampled image. This approach is effective for reducing low-frequency noise but when the filtered image is upsampled to the original resolution, the high frequency part of the image is lost. Further, if this approach is used in the chroma channel, color-bleeding artifacts may occur in the image.
In another example, a noise filter may be applied in the Bayer domain. One known approach is to convert a captured image from the Bayer domain to the color space domain, apply separate noise filters to the luminance and chrominance channels, and convert the filtered image back to the Bayer domain for further processing such as color correction and demosaicing. However, this approach is computationally expensive. Another known approach is to apply a noise filter directly to image in the Bayer domain without separation of the luminance and chrominance information. If a strong noise filter is applied, the strong chrominance noise is reduced but luminance detail is lost. If a weak noise filter is applied, the strong chrominance noise is not suppressed.
Accordingly, improvements in image noise filtering in order to improve the quality of images captured by a digital camera are desirable.